Abstract
In this paper, a real-time tracking-based approach to human action recognition is proposed. The method receives as input depth map data streams from a single kinect sensor. Initially, a skeleton-tracking algorithm is applied. Then, a new action representation is introduced, which is based on the calculation of spherical angles between selected joints and the respective angular velocities. For invariance incorporation, a pose estimation step is applied and all features are extracted according to a continuously updated torso-centered coordinate system; this is different from the usual practice of using common normalization operators. Additionally, the approach includes a motion energy-based methodology for applying horizontal symmetry. Finally, action recognition is realized using Hidden Markov Models (HMMs). Experimental results using the Huawei/3DLife 3D human reconstruction and action recognition Grand Challenge dataset demonstrate the efficiency of the proposed approach.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Hidden Markov Model Toolkit (HTK), http://htk.eng.cam.ac.uk
Ballan, L., Bertini, M., Del Bimbo, A., Seidenari, L., Serra, G.: Effective codebooks for human action representation and classification in unconstrained videos. IEEE Transactions on Multimedia 14(4), 1234–1245 (2012)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12), 2247–2253 (2007)
Gu, J., Ding, X., Wang, S., Wu, Y.: Action and gait recognition from recovered 3-d human joints. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 40(4), 1021–1033 (2010)
Haq, A., Gondal, I., Murshed, M.: On temporal order invariance for view-invariant action recognition. IEEE Transactions on Circuits and Systems for Video Technology 23(2), 203–211 (2013)
Holte, M.B., Chakraborty, B., Gonzalez, J., Moeslund, T.B.: A local 3-d motion descriptor for multi-view human action recognition from 4-d spatio-temporal interest points. IEEE Journal of Selected Topics in Signal Processing 6(5), 553–565 (2012)
Ji, X., Liu, H.: Advances in view-invariant human motion analysis: a review. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(1), 13–24 (2010)
Junejo, I.N., Dexter, E., Laptev, I., Pérez, P.: View-independent action recognition from temporal self-similarities. IEEE Trans. on Pattern Analysis and Machine Intelligence 33(1), 172–185 (2011)
Papadopoulos, G.T., Briassouli, A., Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: Statistical motion information extraction and representation for semantic video analysis. IEEE Transactions on Circuits and Systems for Video Technology 19(10), 1513–1528 (2009)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)
Song, B., Kamal, A.T., Soto, C., Ding, C., Farrell, J.A., Roy-Chowdhury, A.K.: Tracking and activity recognition through consensus in distributed camera networks. IEEE Transactions on Image Processing 19(10), 2564–2579 (2010)
Turaga, P., Veeraraghavan, A., Chellappa, R.: Statistical analysis on stiefel and grassmann manifolds with applications in computer vision. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104(2), 249–257 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Papadopoulos, G.T., Axenopoulos, A., Daras, P. (2014). Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-04114-8_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04113-1
Online ISBN: 978-3-319-04114-8
eBook Packages: Computer ScienceComputer Science (R0)